This analysis examines the stimulus-response characateristics of the SPARS.
Unlike Trial A, where participants were exposed to a prescribed range of stimulus intensities (1 to 4J, at 0.25J intervals), in Trial B, all participants were exposed to 9 stimulus intensities (0.25J interval), but the range of stimuli intensities were calibrated against the sensitivity of each participant. For example, a more sensitive participant may have been exposed to 9 stimuli from 1.75J to 3.75J, another participant may be exposed to stimuli from 2.5J to 4.5J.
This design makes performing group-level analyses difficult (the extremes will have fewer observations), and so we transformed the exposure intensities into an relative scale by ranking (1 to 9) an ordered list of 9 stimulus intensities for each participant. This brought everyone onto the same 1 to 9 scale.
For transparency, we have performed exploratory plots using the raw stimulus intensity data and the relative intensity data. However, the regression analysis was performed using the relative intenisty data only.
# Import
data <- read_rds('./data-cleaned/SPARS_B.rds')
# Inspect
glimpse(data)
## Observations: 2,256
## Variables: 9
## $ PID <chr> "ID06", "ID06", "ID06", "ID06", "ID06", "ID06"...
## $ scale <chr> "SPARS", "SPARS", "SPARS", "SPARS", "SPARS", "...
## $ block_number <int> 2, 2, 2, 4, 4, 4, 6, 6, 6, 8, 8, 8, 11, 11, 11...
## $ trial_number <int> 4, 6, 27, 9, 13, 20, 20, 24, 27, 4, 18, 22, 2,...
## $ intensity <dbl> 1.75, 1.75, 1.75, 1.75, 1.75, 1.75, 1.75, 1.75...
## $ intensity_char <chr> "1.75", "1.75", "1.75", "1.75", "1.75", "1.75"...
## $ intensity_rank <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
## $ rating <int> -49, 2, -6, 3, -20, -2, -31, 2, -5, -8, -23, 1...
## $ rating_positive <dbl> 1, 52, 44, 53, 30, 48, 19, 52, 45, 42, 27, 64,...
We performed a basic clean-up of the data, and then calculated Tukey trimean at each stimulus intensity for each participant (participant average), and finally the median of the trimeans at each stimulus intensity across participants (group average).
############################################################
# #
# Clean #
# #
############################################################
data %<>%
# Rename block_number
rename(block = block_number) %>%
# Select SPARS scale
filter(scale == 'SPARS') %>%
ungroup() %>%
arrange(PID)
############################################################
# #
# Calculate 'Tukey trimean' #
# #
############################################################
# Define tri.mean function
tri.mean <- function(x) {
# Calculate quantiles
q1 <- quantile(x, probs = 0.25, na.rm = TRUE)[[1]]
q2 <- median(x, na.rm = TRUE)
q3 <- quantile(x, probs = 0.75, na.rm = TRUE)[[1]]
# Calculate trimean
tm <- (q2 + ((q1 + q3) / 2)) / 2
# Convert to integer
tm <- as.integer(round(tm))
return(tm)
}
# Calculate the participant average based on 'raw' intensity
data_tm <- data %>%
group_by(PID, intensity) %>%
summarise(tri_mean = tri.mean(rating)) %>%
ungroup()
# Calculate the group average based on 'raw' intensity
data_group <- data_tm %>%
group_by(intensity) %>%
summarise(median = median(tri_mean)) %>%
ungroup()
# Calculate the participant average based on 'relative' intensity
data_tmR <- data %>%
group_by(PID, intensity_rank) %>%
summarise(tri_mean = tri.mean(rating)) %>%
ungroup()
# Calculate the group average based on 'relative' intensity
data_groupR <- data_tmR %>%
group_by(intensity_rank) %>%
summarise(median = median(tri_mean)) %>%
ungroup()
knitr::kable(data %>%
group_by(PID) %>%
summarise(`Minimum stimulus intensity` = min(intensity),
`Maximum stimulus intensity` = max(intensity)),
caption = 'Range of stimulus intensities covered in each participant')
| PID | Minimum stimulus intensity | Maximum stimulus intensity |
|---|---|---|
| ID01 | 2.25 | 4.25 |
| ID02 | 2.25 | 4.25 |
| ID03 | 2.50 | 4.50 |
| ID04 | 2.50 | 4.50 |
| ID05 | 2.50 | 4.50 |
| ID06 | 1.75 | 3.75 |
| ID07 | 2.25 | 4.25 |
# Plot (y.axis = raw stimulus intensity)
data_tm %>%
ggplot(data = .) +
aes(x = intensity,
y = tri_mean) +
geom_point(position = position_jitter(width = 0.05)) +
geom_smooth(method = 'loess',
se = FALSE,
colour = '#666666',
size = 0.6) +
geom_point(data = data_group,
aes(y = median),
shape = 21,
size = 4,
fill = '#D55E00') +
labs(title = 'Group-level stimulus-response plots (raw intensity)',
subtitle = 'Black circles: participant-level Tukey trimeans | Orange circles: group-level median | Grey line: loess curve',
x = 'Stimulus intensity (J)',
y = 'SPARS rating [-50 to 50]') +
scale_y_continuous(limits = c(-50, 50)) +
scale_x_continuous(breaks = seq(from = 1, to = 4, by = 0.5))
# Plot (y.axis = relative stimulus intensity)
data_tmR %>%
ggplot(data = .) +
aes(x = intensity_rank,
y = tri_mean) +
geom_point(position = position_jitter(width = 0.05)) +
geom_smooth(method = 'loess',
se = FALSE,
colour = '#666666',
size = 0.6) +
geom_point(data = data_groupR,
aes(y = median),
shape = 21,
size = 4,
fill = '#D55E00') +
labs(title = 'Group-level stimulus-response plots (relative intensity)',
subtitle = 'Black circles: participant-level Tukey trimeans | Orange circles: group-level median | Grey line: loess curve\nRelative intensity was calculated using the rank of the ordered (ascending) stimulus intensities each participant was exposed to.',
x = 'Relative stimulus intensity',
y = 'SPARS rating [-50 to 50]') +
scale_y_continuous(limits = c(-50, 50)) +
scale_x_continuous(breaks = seq(from = 1, to = 9, by = 1))
# Plot (y.axis = raw stimulus intensity)
data %>%
ggplot(data = .) +
aes(x = intensity,
y = rating) +
geom_point() +
geom_smooth(method = 'loess',
se = FALSE,
colour = '#666666',
size = 0.6) +
geom_point(data = data_tm,
aes(y = tri_mean),
shape = 21,
size = 3,
fill = '#D55E00') +
labs(title = 'Participant-level stimulus-response plot (raw intensity)',
subtitle = 'Black circles: individual experimental blocks | Orange circles: Tukey trimean | Grey line: loess curve',
x = 'Stimulus intensity (J)',
y = 'SPARS rating [-50 to 50]') +
scale_y_continuous(limits = c(-50, 50)) +
facet_wrap(~ PID, ncol = 4)
# Plot (y.axis = rank stimulus intensity)
data %>%
ggplot(data = .) +
aes(x = intensity_rank,
y = rating) +
geom_point() +
geom_smooth(method = 'loess',
se = FALSE,
colour = '#666666',
size = 0.6) +
geom_point(data = data_tmR,
aes(y = tri_mean),
shape = 21,
size = 3,
fill = '#D55E00') +
labs(title = 'Participant-level stimulus-response plot (relative intensity)',
subtitle = 'Black circles: individual experimental blocks | Orange circles: Tukey trimean | Grey line: loess curve\nRelative intensity was calculated using the rank of the ordered (ascending) stimulus intensities each participant was exposed to.',
x = 'Relative stimulus intensity',
y = 'SPARS rating [-50 to 50]') +
scale_y_continuous(limits = c(-50, 50)) +
facet_wrap(~ PID, ncol = 4)
# Process data (raw stimulus intensity)
data_block <- data %>%
# Rename blocks
#mutate(block = sprintf('Block: %s (order: %i)', block, block_order)) %>%
# Nest by PID
group_by(PID) %>%
nest() %>%
# Generate plots
mutate(plots = map2(.x = data,
.y = unique(PID),
~ ggplot(data = .x) +
aes(x = intensity,
y = rating) +
geom_point() +
geom_smooth(method = 'loess',
se = FALSE,
colour = '#666666',
size = 0.6) +
labs(title = paste(.y, ': Participant-level stimulus-response plots conditioned on experimental block (raw intensity'),
subtitle = 'Black circles: individual data points | Grey line: loess curve',
x = 'Stimulus intensity (J)',
y = 'SPARS rating [-50 to 50]') +
scale_y_continuous(limits = c(-50, 50)) +
facet_wrap(~ block, ncol = 2)))
# Print plots
walk(.x = data_block$plots, ~ print(.x))
# Process data (relative stimulus intensity)
data_blockR <- data %>%
# Rename blocks
#mutate(block = sprintf('Block: %s (order: %i)', block, block_order)) %>%
# Nest by PID
group_by(PID) %>%
nest() %>%
# Generate plots
mutate(plots = map2(.x = data,
.y = unique(PID),
~ ggplot(data = .x) +
aes(x = intensity_rank,
y = rating) +
geom_point() +
geom_smooth(method = 'loess',
se = FALSE,
colour = '#666666',
size = 0.6) +
labs(title = paste(.y, ': Participant-level stimulus-response plots conditioned on experimental block (relative intensity'),
subtitle = 'Black circles: individual data points | Grey line: loess curve\nRelative intensity was calculated using the rank of the ordered (ascending) stimulus intensities each participant was exposed to.',
x = 'Relative stimulus intensity',
y = 'SPARS rating [-50 to 50]') +
scale_y_continuous(limits = c(-50, 50)) +
facet_wrap(~ block, ncol = 2)))
# Print plots
walk(.x = data_blockR$plots, ~ print(.x))
To allow for a curvilinear relationship between stimulus intensity and rating, we modelled the data using polynomial regression, with 1st (linear), 2nd (quadratic), and 3rd (cubic) order orthogonal polynomials. For each polynomial expression, we modelled the random effects as random intercept only, and as random intercept and slope.
The random intercept only and random intercept and slope models were compared using the logliklihood test, and the better model taken foward. Diagnostics were run on the final model only, and we examined level 1 residuals (conditional / fixed effects), and level 2 residuals (random effects) and influence points 1.
# Intercept only
lmm1 <- lmer(tri_mean ~ intensity_rank + (1 | PID),
data = data_tmR,
REML = TRUE)
# Intercept and slope
lmm1b <- lmer(tri_mean ~ intensity_rank + (intensity_rank | PID),
data = data_tmR,
REML = TRUE)
# Better model?
anova(lmm1, lmm1b)
## Data: data_tmR
## Models:
## lmm1: tri_mean ~ intensity_rank + (1 | PID)
## lmm1b: tri_mean ~ intensity_rank + (intensity_rank | PID)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## lmm1 4 495.89 504.46 -243.94 487.89
## lmm1b 6 475.23 488.08 -231.61 463.23 24.66 2 4.418e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Anova of better model
Anova(lmm1b,
type = 2,
test.statistic = 'F')
## Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)
##
## Response: tri_mean
## F Df Df.res Pr(>F)
## intensity_rank 28.612 1 6 0.001746 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Print better model
sjt.lmer(lmm1b,
show.header = TRUE,
string.dv = "Response",
string.pred = "Coefficients",
depvar.labels = '',
pred.labels = 'intensity_rank',
string.est = 'Estimate',
string.ci = '95% CI',
string.p = 'p-value',
show.icc = FALSE,
show.r2 = FALSE)
| Coefficients | Response | |||
| Estimate | 95% CI | p-value | ||
| Fixed Parts | ||||
| (Intercept) | -38.87 | -56.10 – -21.65 | .004 | |
| intensity_rank | 6.01 | 3.81 – 8.21 | .002 | |
| Random Parts | ||||
| σ2 | 58.333 | |||
| τ00, PID | 509.896 | |||
| ρ01 | -0.958 | |||
| NPID | 7 | |||
| Observations | 63 | |||
# Intercept only
lmm2 <- lmer(tri_mean ~ poly(intensity_rank, 2) + (1 | PID),
data = data_tmR,
REML = TRUE)
# Intercept and slope
lmm2b <- lmer(tri_mean ~ poly(intensity_rank, 2) + (intensity_rank | PID),
data = data_tmR,
REML = TRUE)
# Better model?
anova(lmm2, lmm2b)
## Data: data_tmR
## Models:
## lmm2: tri_mean ~ poly(intensity_rank, 2) + (1 | PID)
## lmm2b: tri_mean ~ poly(intensity_rank, 2) + (intensity_rank | PID)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## lmm2 5 495.50 506.22 -242.75 485.50
## lmm2b 7 472.71 487.71 -229.35 458.71 26.793 2 1.521e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Anova for better model
Anova(lmm2b,
type = 2,
test.statistic = 'F')
## Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)
##
## Response: tri_mean
## F Df Df.res Pr(>F)
## poly(intensity_rank, 2) 15.85 2 13.527 0.0002848 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Print better model
sjt.lmer(lmm2b,
show.header = TRUE,
string.dv = "Response",
string.pred = "Coefficients",
depvar.labels = '',
pred.labels = 'intensity_rank',
string.est = 'Estimate',
string.ci = '95% CI',
string.p = 'p-value',
show.icc = FALSE,
show.r2 = FALSE)
| Coefficients | Response | |||
| Estimate | 95% CI | p-value | ||
| Fixed Parts | ||||
| (Intercept) | -8.83 | -16.47 – -1.18 | .064 | |
| poly(intensity_rank, 2)1 | 123.16 | 78.03 – 168.29 | .002 | |
| poly(intensity_rank, 2)2 | 15.87 | 1.42 – 30.31 | .075 | |
| Random Parts | ||||
| σ2 | 54.304 | |||
| τ00, PID | 512.022 | |||
| ρ01 | -0.957 | |||
| NPID | 7 | |||
| Observations | 63 | |||
# Intercept only
lmm3 <- lmer(tri_mean ~ poly(intensity_rank, 3) + (1 | PID),
data = data_tmR,
REML = TRUE)
# Intercept and slope
lmm3b <- lmer(tri_mean ~ poly(intensity_rank, 3) + (intensity_rank | PID),
data = data_tmR,
REML = TRUE)
# Better model?
anova(lmm3, lmm3b)
## Data: data_tmR
## Models:
## lmm3: tri_mean ~ poly(intensity_rank, 3) + (1 | PID)
## lmm3b: tri_mean ~ poly(intensity_rank, 3) + (intensity_rank | PID)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## lmm3 6 497.33 510.19 -242.66 485.33
## lmm3b 8 474.37 491.52 -229.19 458.37 26.956 2 1.402e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Anova for better model
Anova(lmm3b,
type = 2,
test.statistic = 'F')
## Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)
##
## Response: tri_mean
## F Df Df.res Pr(>F)
## poly(intensity_rank, 3) 10.617 3 21.094 0.0001842 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Print better model
sjt.lmer(lmm3b,
show.header = TRUE,
string.dv = "Response",
string.pred = "Coefficients",
depvar.labels = '',
pred.labels = 'intensity_rank',
string.est = 'Estimate',
string.ci = '95% CI',
string.p = 'p-value',
show.icc = FALSE,
show.r2 = FALSE)
| Coefficients | Response | |||
| Estimate | 95% CI | p-value | ||
| Fixed Parts | ||||
| (Intercept) | -8.83 | -16.47 – -1.18 | .064 | |
| poly(intensity_rank, 3)1 | 123.16 | 78.03 – 168.29 | .002 | |
| poly(intensity_rank, 3)2 | 15.87 | 1.32 – 30.41 | .076 | |
| poly(intensity_rank, 3)3 | -4.22 | -18.76 – 10.33 | .590 | |
| Random Parts | ||||
| σ2 | 55.081 | |||
| τ00, PID | 511.612 | |||
| ρ01 | -0.957 | |||
| NPID | 7 | |||
| Observations | 63 | |||
knitr::kable(broom::tidy(anova(lmm1b, lmm2b, lmm3b)),
caption = 'Linear model vs quadratic model and cubic model')
| term | df | AIC | BIC | logLik | deviance | statistic | Chi.Df | p.value |
|---|---|---|---|---|---|---|---|---|
| lmm1b | 6 | 475.2254 | 488.0842 | -231.6127 | 463.2254 | NA | NA | NA |
| lmm2b | 7 | 472.7085 | 487.7104 | -229.3542 | 458.7085 | 4.5169285 | 1 | 0.0335610 |
| lmm3b | 8 | 474.3731 | 491.5182 | -229.1866 | 458.3731 | 0.3353413 | 1 | 0.5625307 |
predicted <- ggeffects::ggpredict(model = lmm2b,
terms = 'intensity_rank',
ci.lvl = 0.95)
ggplot() +
geom_ribbon(data = predicted,
aes(x = x,
ymin = conf.low,
ymax = conf.high),
fill = '#cccccc') +
geom_line(data = predicted,
aes(x = x,
y = predicted)) +
geom_point(data = predicted,
aes(x = x,
y = predicted)) +
geom_point(data = data_groupR,
aes(x = intensity_rank,
y = median),
shape = 21,
size = 4,
fill = '#D55E00') +
labs(title = 'Quadratic model (95% CI): Predicted values vs stimulus intensity_rank',
subtitle = 'Black circles/line: predicted values | Orange circles: group-level median\nRelative intensity was calculated using the rank of the ordered (ascending) stimulus intensities each participant was exposed to.',
x = 'Relative stimulus intensity',
y = 'SPARS rating [-50 to 50]') +
scale_y_continuous(limits = c(-50, 50)) +
scale_x_continuous(breaks = seq(from = 1, to = 9, by = 1))
The quadratic model has the best fit. We performed diagnostics on this model to confirm that the model was properly specified.
# Level 1 residuals
## Standardized
lmm_resid1 <- HLMresid(lmm2b,
level = 1,
type = 'LS',
standardize = TRUE)
# Semi-standardized residuals (used for assessing homoscedasticity)
lmm_ssresid1 <- HLMresid(lmm2b,
level = 1,
type = 'LS',
standardize = 'semi')
# Level 2 residuals
## Standardized
lmm_resid2 <- HLMresid(lmm2b,
level = 'PID',
type = 'EB')
The relationship between predictor(s) and outcome for a linear model should be linear. This relationship can be observed by plotting the level 1 standardized residuals against the predictors. The scatter of residuals should show no pattern, and be centered around 0.
# Standardized residuals vs intensity_rank
ggplot(data = lmm_resid1) +
aes(x = `poly(intensity_rank, 2)`[, 1],
y = std.resid) +
geom_point() +
geom_smooth(method = 'lm') +
geom_hline(yintercept = 0) +
geom_hline(yintercept = -2,
linetype = 2) +
geom_hline(yintercept = 2,
linetype = 2) +
labs(title = 'Quadratic model: Level 1 residuals vs intensity_rank',
subtitle = 'Assess linearity of the intensity_rank term | Blue line: linear regression line',
caption = 'The regression line should be centered on 0\n~95% of points should be betwen -2 and +2',
y = 'Standardized residuals',
x = 'Stimulus intensity_rank')
# Standardized residuals vs intensity^2
ggplot(data = lmm_resid1) +
aes(x = `poly(intensity_rank, 2)`[, 2],
y = std.resid) +
geom_point() +
geom_smooth(method = 'lm') +
geom_hline(yintercept = 0) +
geom_hline(yintercept = -2,
linetype = 2) +
geom_hline(yintercept = 2,
linetype = 2) +
labs(title = expression(paste('Quadratic model: Level 1 residuals vs ', intensity^2)),
subtitle = 'Assess linearity of the intensity_rank^2 term | Blue line: linear regression line\nRelative intensity was calculated using the rank of the ordered (ascending) stimulus intensities each participant was exposed to.',
caption = 'The regression line should be centered on 0\n~95% of points should be betwen -2 and +2',
y = 'Standardized residuals',
x = expression(Relative~stimulus~intensity^2))
Based on the plot of the linear and quadratic terms’ residuals, we accept that the condition of linearity for the quadratic model.
The variance of residuals should be constant across the range of the predictor(s). This relationship can be observed by plotting the level 1 semi-standardized residuals against the predictors. Like the assessment of linearity, the residuals should be centered on 0, and show no pattern in the scatter of points.
# Standardized residuals vs intensity_rank
ggplot(data = lmm_ssresid1) +
aes(x = `poly(intensity_rank, 2)`[ ,1],
y = semi.std.resid) +
geom_point() +
geom_hline(yintercept = 0) +
labs(title = 'Quadratic model: Level 1 residuals vs intensity_rank',
subtitle = 'Assess homoscedasticity for the intensity_rank term',
y = 'Semi-standardized residuals',
x = 'Relative stimulus intensity')
# Standardized residuals vs intensity^2
ggplot(data = lmm_ssresid1) +
aes(x = `poly(intensity_rank, 2)`[, 2],
y = semi.std.resid) +
geom_point() +
geom_smooth(method = 'lm') +
geom_hline(yintercept = 0) +
labs(title = expression(paste('Quadratic model: Level 1 residuals vs ', intensity^2)),
subtitle = 'Assess homoscedasticity for the intensity_rank^2 term | Blue line: linear regression line\nRelative intensity was calculated using the rank of the ordered (ascending) stimulus intensities each participant was exposed to.',
y = 'Semi-standardized residuals',
x = expression(Relative~stimulus~intensity^2))
There is no obvious pattern to the scatter of residuals across any of the fixed effect terms. So we accept that the residuals are homoscedastic in the quadratic model.
Residuals should be normally distributed. There are various methods of examining the distribution, and we have chosen the QQ-plot method, which plots the quantiles of the standardized residuals against a theoretical (Gaussian) quantile distribution. Points should line on the line of identity of the two sets of quantiles follow the same distribution.
# Standardized residuals vs intensity_rank
ggplot_qqnorm(x = lmm_resid1$std.resid,
line = "rlm") +
labs(title = 'Quadratic model: QQ-plot of level 1 residuals',
subtitle = 'Assessing whether residuals follow a normal distribution',
x = 'Theoretical quantiles',
y = 'Standardized residuals')
There is minor deviation at the extremes (possibly a thin left tail), but on the whole, we are satisfied that the quadratic model fits the assumption of normally distributed residuals.
Level 2 residuals can be used to identify predictors that should be included in the model, but since we are only assessing the effect of stimulus strength on SPARS rating, we have only assessed whether the level 2 residuals (intercept and slope) meet the assumption of being normally distributed (assessed using QQ-plots).
# Generate QQplots
qq1 <- ggplot_qqnorm(x = lmm_resid2$`(Intercept)`,
line = "rlm") +
labs(title = 'Quadratic model: QQ-plot of level 2 residuals (Intercept)',
subtitle = 'Assessing whether residuals follow a normal distribution',
x = 'Theoretical quantiles',
y = 'Residuals')
qq2 <- ggplot_qqnorm(x = lmm_resid2$intensity_rank,
line = "rlm") +
labs(title = 'Quadratic model: QQ-plot of level 2 residuals (slope: intensity_rank)',
subtitle = 'Assessing whether residuals follow a normal distribution',
x = 'Theoretical quantiles',
y = 'Residuals')
# Plot
qq1 + qq2
Although the data are sparse, we are satisfied that the level 2 residuals for the intercept and the slope of the quadratic model fit the assumption of being normally distributed.
We assessed three aspects of influence (data that significantly model coefficients):
The variance component (random effects) was assesed using the relative variance change metric, which calculates the impact of deleting observational units of the variance of the residuals, random intercept, random slope, and covariance of the random slope and random intercept.
Leverage was used to assess fitted values. The assessment involves assessing the rate of change in the predicted response with respect to the observed response.
Cook’s Distance was used to assess the influence of fixed effects. The metric measures the distance between the fixed effects estimates obtained from the full model to that obtained from the reduced data (observations removed).
In all cases, we treated the individual (indicated using PID) as the unit of observation, and we used internal scaling to set the diagnostic cutoffs for each metric. The cutoffs were determined as: \(3^{rd}~Quartile + (3 \cdot IQR)\).
# Prepare relative variance change (RCV)
influence_rvc <- rvc(lmm1b,
group = 'PID')
# Prepare Cook's distance
influence_cooks <- cooks.distance(lmm1b,
group = 'PID')
# Prepare leverage
## (Assessed at the level of PID, and not the individual observation)
influence_leverage <- leverage(lmm1b,
level = 'PID')
Estimation of the variance component was undertaken by calculating relative variance change (RCV). RVC is close to zero when deletion of observational units from the model does not have a large infuence on the variance component.
# Plot
dotplot_diag(x = influence_rvc[ , 1],
cutoff = 'internal',
name = 'rvc') +
labs(title = 'Relative variance change for the residual variance',
subtitle = 'Cutoffs determined by measures of internal scaling',
y = 'Relative variance change',
x = 'Participant ID')
dotplot_diag(x = influence_rvc[ , 2],
cutoff = 'internal',
name = 'rvc') +
labs(title = 'Relative variance change for the random intercept variance',
subtitle = 'Cutoffs determined by measures of internal scaling',
y = 'Relative variance change',
x = 'Participant ID')
dotplot_diag(x = influence_rvc[ , 3],
cutoff = 'internal',
name = 'rvc') +
labs(title = 'Relative variance change for the random slope variance',
subtitle = 'Cutoffs determined by measures of internal scaling',
y = 'Relative variance change',
x = 'Participant ID')
dotplot_diag(x = influence_rvc[ , 4],
cutoff = 'internal',
name = 'rvc') +
labs(title = 'Relative variance change for the random slope and intercept covariance',
subtitle = 'Cutoffs determined by measures of internal scaling',
y = 'Relative variance change',
x = 'Participant ID')
One value (PID11) is below the cutoff for the relative variance change for random slope and intercept covariance. The extent of the deviation is minor, and was ignored.
Assessing whether observations are unusual with regard to the fitted values and explanatory variables using leverage. We assessed leverage at two levels: i) fixed effects, and ii) unconfounded (by fixed effects) random effects.
dotplot_diag(x = influence_leverage[, 2],
cutoff = "internal",
name = "leverage") +
labs(title = 'Leverage: fixed effects',
subtitle = 'Cutoffs determined by measures of internal scaling',
y = 'Leverage',
x = 'Participant ID')
dotplot_diag(x = influence_leverage[, 4],
cutoff = "internal",
name = "leverage") +
labs(title = 'Leverage: unconfounded random effects',
subtitle = 'Cutoffs determined by measures of internal scaling',
y = 'Leverage',
x = 'Participant ID')
Influence points were assessed by calculating Cook’s Distance metrics.
# Plot data
dotplot_diag(x = influence_cooks,
cutoff = "internal",
name = "cooks.distance") +
labs(title = 'Influence: Cooks Distance',
subtitle = 'Cutoffs determined by measures of internal scaling',
y = 'Cooks Distance',
x = 'Participant ID')
Based on There are no influential fixed effects.
The linear is well-specified.
# Quantile model with 2.5, 25, 50, 75, and 97.5% quantiles
qmm <- lqmm(fixed = tri_mean ~ poly(intensity_rank, 2),
random = ~ intensity_rank,
group = PID,
data = data_tmR,
tau = c(0.025, 0.25, 0.5, 0.75, 0.975))
# Summary
summary(qmm)
## Call: lqmm(fixed = tri_mean ~ poly(intensity_rank, 2), random = ~intensity_rank,
## group = PID, tau = c(0.025, 0.25, 0.5, 0.75, 0.975), data = data_tmR)
##
## tau = 0.025
##
## Fixed effects:
## Value Std. Error lower bound upper bound
## (Intercept) -69.078 22.134 -113.558 -24.599
## poly(intensity_rank, 2)1 128.368 18.632 90.924 165.811
## poly(intensity_rank, 2)2 11.399 14.729 -18.201 40.998
## Pr(>|t|)
## (Intercept) 0.00302 **
## poly(intensity_rank, 2)1 9.84e-09 ***
## poly(intensity_rank, 2)2 0.44272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## tau = 0.25
##
## Fixed effects:
## Value Std. Error lower bound upper bound
## (Intercept) -17.4995 8.6741 -34.9308 -0.0682
## poly(intensity_rank, 2)1 121.5351 19.2075 82.9361 160.1341
## poly(intensity_rank, 2)2 5.7801 14.6551 -23.6705 35.2307
## Pr(>|t|)
## (Intercept) 0.04914 *
## poly(intensity_rank, 2)1 7.305e-08 ***
## poly(intensity_rank, 2)2 0.69499
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## tau = 0.5
##
## Fixed effects:
## Value Std. Error lower bound upper bound
## (Intercept) -4.4901 5.8081 -16.1619 7.1817
## poly(intensity_rank, 2)1 121.6123 19.9464 81.5285 161.6961
## poly(intensity_rank, 2)2 13.7000 14.7175 -15.8759 43.2759
## Pr(>|t|)
## (Intercept) 0.4432
## poly(intensity_rank, 2)1 1.659e-07 ***
## poly(intensity_rank, 2)2 0.3565
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## tau = 0.75
##
## Fixed effects:
## Value Std. Error lower bound upper bound
## (Intercept) 2.4089 12.2317 -22.1717 26.989
## poly(intensity_rank, 2)1 118.7761 22.3803 73.8013 163.751
## poly(intensity_rank, 2)2 17.9548 15.5720 -13.3383 49.248
## Pr(>|t|)
## (Intercept) 0.8447
## poly(intensity_rank, 2)1 2.676e-06 ***
## poly(intensity_rank, 2)2 0.2545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## tau = 0.975
##
## Fixed effects:
## Value Std. Error lower bound upper bound
## (Intercept) 60.212 20.260 19.497 100.926
## poly(intensity_rank, 2)1 117.400 19.658 77.897 156.904
## poly(intensity_rank, 2)2 17.274 14.273 -11.409 45.956
## Pr(>|t|)
## (Intercept) 0.004577 **
## poly(intensity_rank, 2)1 2.583e-07 ***
## poly(intensity_rank, 2)2 0.232001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Null model (likelihood ratio):
## [1] 33.963 (p = 4.218e-08) 70.106 (p = 5.551e-16) 25.873 (p = 2.409e-06)
## [4] 3.623 (p = 1.634e-01) 29.445 (p = 4.037e-07)
## AIC:
## [1] 585.5 (df = 6) 509.7 (df = 6) 522.3 (df = 6) 524.7 (df = 6)
## [5] 562.1 (df = 6)
# Get predicted values
## Level 0 (conditional, note difference to the lmer diagnostics)
quant_predict <- as.data.frame(predict(qmm, level = 0))
names(quant_predict) <- paste0('Q', c(2.5, 25, 50, 75, 97.5))
# Join with 'central_lmm'
data_lqmm <- data_tmR %>%
bind_cols(quant_predict)
# Trim prediction to upper and lower limits of the scale
data_lqmm %<>%
mutate_if(is.numeric,
funs(ifelse(. > 50,
yes = 50,
no = ifelse(. < -50,
yes = -50,
no = .))))
# Plot
ggplot(data = data_lqmm) +
aes(x = intensity_rank,
y = Q50) +
geom_ribbon(aes(ymin = `Q2.5`,
ymax = `Q97.5`),
fill = '#E69F00') +
geom_ribbon(aes(ymin = `Q25`,
ymax = `Q75`),
fill = '#56B4E9') +
geom_point(size = 3,
shape = 21,
fill = '#FFFFFF',
colour = '#000000') +
geom_hline(yintercept = 0,
linetype = 2) +
labs(title = paste('Quantile regression'),
subtitle = 'Open circles: 50th percentile (median) | Blue band: interquartile range | Orange band: 95% prediction interval',
x = 'Realtive stimulus intensity',
y = 'SPARS rating [-50 to 50]') +
scale_y_continuous(limits = c(-50, 50)) +
scale_x_continuous(breaks = unique(data_lqmm$intensity_rank))
## With original data
ggplot(data = data_lqmm) +
aes(x = intensity_rank,
y = Q50) +
geom_ribbon(aes(ymin = `Q2.5`,
ymax = `Q97.5`),
fill = '#E69F00') +
geom_ribbon(aes(ymin = `Q25`,
ymax = `Q75`),
fill = '#56B4E9') +
geom_point(data = data_tmR,
aes(y = tri_mean),
position = position_jitter(width = 0.03)) +
geom_point(size = 3,
shape = 21,
fill = '#FFFFFF',
colour = '#000000') +
geom_hline(yintercept = 0,
linetype = 2) +
labs(title = paste('Quantile regression (with original Tukey trimean data)'),
subtitle = 'Open circles: 50th percentile (median) | Blue band: interquartile range | Orange band: 95% prediction interval',
x = 'Stimulus intensity_rank (J)',
y = 'SPARS rating [-50 to 50]') +
scale_y_continuous(limits = c(-50, 50)) +
scale_x_continuous(breaks = unique(data_lqmm$intensity_rank))
The response is consistent across the range of stimulus intensities, but the preduction interval is extremely broad.
sessionInfo()
## R version 3.4.3 (2017-11-30)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.3
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
##
## attached base packages:
## [1] methods stats graphics grDevices utils datasets base
##
## other attached packages:
## [1] bindrcpp_0.2 car_2.1-6 sjPlot_2.4.1
## [4] HLMdiag_0.3.1 lqmm_1.5.3 lme4_1.1-15
## [7] Matrix_1.2-12 patchwork_0.0.1 forcats_0.2.0
## [10] stringr_1.2.0 dplyr_0.7.4 purrr_0.2.4
## [13] readr_1.1.1 tidyr_0.8.0 tibble_1.4.2
## [16] ggplot2_2.2.1.9000 tidyverse_1.2.1 magrittr_1.5
##
## loaded via a namespace (and not attached):
## [1] TH.data_1.0-8 minqa_1.2.4 colorspace_1.3-2
## [4] modeltools_0.2-21 sjlabelled_1.0.7 rprojroot_1.3-2
## [7] estimability_1.2 snakecase_0.8.1 rstudioapi_0.7
## [10] glmmTMB_0.2.0 MatrixModels_0.4-1 DT_0.4
## [13] mvtnorm_1.0-7 lubridate_1.7.1 coin_1.2-2
## [16] xml2_1.2.0 codetools_0.2-15 splines_3.4.3
## [19] mnormt_1.5-5 knitr_1.19 sjmisc_2.7.0
## [22] effects_4.0-0 bayesplot_1.4.0 jsonlite_1.5
## [25] nloptr_1.0.4 ggeffects_0.3.1 pbkrtest_0.4-7
## [28] broom_0.4.3 shiny_1.0.5 compiler_3.4.3
## [31] httr_1.3.1 sjstats_0.14.1 emmeans_1.1
## [34] backports_1.1.2 assertthat_0.2.0 lazyeval_0.2.1
## [37] survey_3.33 cli_1.0.0 quantreg_5.34
## [40] htmltools_0.3.6 tools_3.4.3 SparseGrid_0.8.2
## [43] coda_0.19-1 gtable_0.2.0 glue_1.2.0
## [46] reshape2_1.4.3 merTools_0.3.0 Rcpp_0.12.15
## [49] carData_3.0-0 cellranger_1.1.0 nlme_3.1-131
## [52] psych_1.7.8 lmtest_0.9-35 rvest_0.3.2
## [55] mime_0.5 stringdist_0.9.4.6 MASS_7.3-48
## [58] zoo_1.8-1 scales_0.5.0.9000 hms_0.4.1
## [61] parallel_3.4.3 sandwich_2.4-0 SparseM_1.77
## [64] pwr_1.2-1 TMB_1.7.12 yaml_2.1.16
## [67] stringi_1.1.6 highr_0.6 blme_1.0-4
## [70] rlang_0.1.6 pkgconfig_2.0.1 arm_1.9-3
## [73] evaluate_0.10.1 lattice_0.20-35 prediction_0.2.0
## [76] bindr_0.1 labeling_0.3 htmlwidgets_1.0
## [79] tidyselect_0.2.3 plyr_1.8.4 R6_2.2.2
## [82] multcomp_1.4-8 RLRsim_3.1-3 withr_2.1.1.9000
## [85] pillar_1.1.0 haven_1.1.1 foreign_0.8-69
## [88] mgcv_1.8-23 survival_2.41-3 abind_1.4-5
## [91] nnet_7.3-12 modelr_0.1.1 crayon_1.3.4
## [94] rmarkdown_1.8 grid_3.4.3 readxl_1.0.0
## [97] digest_0.6.15 xtable_1.8-2 httpuv_1.3.5
## [100] stats4_3.4.3 munsell_0.4.3